Underlying a lot of intelligent behavior is the ability to balance the value and costs of collecting information in advance of taking an action or set of actions. Calculating the expected value of information (VOI) for sequences of observations under uncertainty is intractable, as branching trees of potential outcomes of sets of observations need to be considered in the general case. The task involves computing expectations over an exponentially growing tree of future evidence-gathering actions and outcomes
Existing ways to use VOI approximations include making calculations of the value of a single “next” test to guide decision-making. These approximations to VOI do not work very well, as they rely on the assumption that only a single piece of evidence will be observed in advance of action, but are nevertheless used in sequential information-gathering settings. Is sum, real-world tasks can pose unsolvable problems with available methods for computing VOI to guide observations.